Optimal powertrain integration and control design are essential to developing more fuel efficient vehicles. Vehicle systems are becoming increasingly complex, as are driver expectations for both fuel economy and performance. Shorter product development times result in less time available to evaluate alternative powertrain hardware configurations and related control strategies. Often, the interrelationship between hardware and control design and the dependence on driver application of each is overlooked.
Simulation and optimization of vehicle systems are now being utilized more as vehicle systems become increasingly complex and as available product development time decreases. Known in the art are various methods and systems for powertrain optimization and improved fuel economy. These methods and systems, however, rely heavily on control parameters, such as a shift map, which may not exist for hypothetical vehicle designs. Additionally, such methods and systems do not take the driving style or drive cycle characteristics into account. Known methods and systems also do not have the ability to optimize hardware and control design simultaneously.
Vehicle system simulations known in the art employ either a forward or backward-looking approach. A forward-looking simulation includes a driver model and iteratively alters vehicle subsystem and component commands until the desired response of the system is achieved. The driver model considers the current and required speed to determine the appropriate throttle and brake commands often using a PI controller (proportional (P) and integral (I) controls). The throttle command is translated into a fuel flow rate and engine torque, which is subsequently inputted into the transmission model, where the transmission output torque is computed from the transmission's efficiency and gear ratio. The transmission output torque is propagated forward through the drivetrain until the tractive force at the road and corresponding acceleration is calculated.
Backward-looking models generally assume the vehicle meets the desired driver trace and therefore do not require a driver model. Contrary to the forward-looking approach, the force required to achieve the corresponding acceleration is directly calculated step-by-step from the desired speed trace. The required force is then converted into the required torque and rotational speed that must be provided by the component directly upstream. This calculation approach is continued in the reverse direction of the road load tractive force through the drivetrain until the energy demand that would be necessary to meet the driver trace is determined.
Advanced vehicle models have become an essential tool to evaluate vehicle system performance early in the design phase. The National Renewable Energy Laboratory developed the Advanced Vehicle Simulator (ADVISOR), a backward-looking model, to quantify the fuel economy, performance and emissions of vehicles including alternative technologies (Markel, T., A, Brooker, T. Hendricks, V. Johnson, V., K. Kelly, B. Kramer, M. O'Keefe, S. Sprik, and K. Wipke (2002). “ADVISOR, A Systems Analysis Tool for Advanced Vehicle Modeling,” Journal of Power Sources, 110, 255-266.). Argonne National Laboratory under the direction of the Partnership for a New Generation (PGNV) developed the Vehicle Systems Analysis Toolkit (P-SAT), a forward-looking simulation that calculates the power generated by the powertrain by modeling the driver following a pre-defined cycle (Rousseau, A., S. Pagerit, G. Monnet, and A. Feng (2001). “The New PNGV System Analysis Toolkit PSAT v4.1—Evolution and Improvement,” SAE Paper 2001-01-2536.). The Automotive Research Center at the University of Michigan developed a Vehicle Engine Simulation (VESIM) composed of forward-looking engine, driveline, and vehicle dynamics modules to simulate the dynamic response of a heavy duty diesel truck (Assam's, D., Z. Filipi, S. Gravante, S. Grohnke, X. Gui, L. Louca, G, Rideout, J. Stein, and Y. Wang (2000). “Validation and Use of SIMULINK Integrated, High Fidelity, Engine-In-Vehicle Simulation of the International Class VI Truck,” SAE Paper 2000-01-0288.). An array of other modeling software and tools has been developed both commercially and in academia. One example is the Rapid Automotive Performance Simulator (RAPTOR) co-developed by Southwest Research Institute and DaimlerChrysler used for virtual powertrain fuel economy predictions (Berry, A., M. Blissett, J. Steiber, A. Tobin, and S. McBroom (2002). “A New Approach to Improving Fuel Economy and Performance Prediction through Coupled Thermal Systems Simulation,” SAE Paper 2002-01-1208).
Early vehicle design optimization work concentrated on optimizing engine control parameters. Auiler et al used a reverse power flow model to optimize the air/fuel ratio, spark timing, and percentage of exhaust gas recirculation in the engine calibration using dynamic programming to allocate emissions contributions while maximizing fuel economy (Auiler, J. E., J. D. Zbrozek, and P. N. Blumberg, (1977). “Optimization of Automotive Engine Calibration for Better Fuel Economy—Methods and Applications,” SAE Paper 770076.). Initial work involving powertrain matching techniques for improving fuel economy was performed by Wong and Clemens (Wong, L. T. and W. J. Clemens (1979). “Powertrain Matching for Better Fuel Economy” SAE Paper 790045.) and Porter (Porter, F. C. (1979) “Design for Fuel Economy—The New GM Front Drive Cars,” SAE Paper 790721.). Dynamic programming was later applied to find the optimal gear shift sequence and the power split for a hybrid electrical truck (Lin C. C., H. Peng, J. W. Grizzle, and J. M. Kang (2003) “Power Management Strategy for a Parallel Hybrid Electric Truck,” IEEE Transactions on Control Systems Technology, 11, 839-849.). Kim used a forward-looking model and dynamic programming to optimize shift maps for fuel economy based on constant throttle inputs (Kim, D. (2006), “Math-Model Based Gear-Shift Control Strategy for Advanced Vehicle Powertrain Systems,” University of Michigan Ph.D. Dissertation.).
While optimization in vehicle system design is growing, there still exists a need to fully explore the capabilities of the powertrain system by developing a model-based approach that combines optimal powertrain hardware configuration with optimal control. The technology described herein, including, for example, matching the powertrain hardware configuration and the transmission gear shift and torque converter clutch control strategies to specific vehicle and drive cycle attributes, provides such a solution.